A Cluster Mean Approach for Topology Optimization of Natural Frequencies and Bandgaps With Simple/Multiple Eigenfrequencies

Abstract

This study presents a novel approach utilizing cluster means to address the non-differentiability issue arising from multiple eigenvalues in eigenfrequency and bandgap optimization. This study builds upon the method proposed by Zhang et al., extending it to eigenfrequency topology optimization using bound formulations. By constructing symmetric functions of repeated eigenvalues—including cluster mean, $p$-norm and Kreisselmeier–Steinhauser (KS) functions—the study confirms their differentiability when all repeated eigenvalues are included, that is, clusters are complete. Numerical sensitivity analyses indicate that, under some symmetry conditions, multiple eigenvalues may also be differentiable with respect to the symmetric design variables. Notably, regardless of enforced symmetry, the cluster mean approach guarantees the differentiability of multiple eigenvalues, offering a reliable solution strategy in eigenfrequency optimization. Optimization schemes are proposed to maximize eigenfrequencies and bandgaps by integrating cluster means with the bound formulations. The efficacy of the proposed method is demonstrated through numerical examples of 2D and 3D solids and plate structures. All optimization results demonstrate smooth convergence under simple/multiple eigenvalues.